Production Machine Learning Systems

This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where "Machine Learning on GCP" left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended

( 51 Reviews )

Created by: Google Cloud Training

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Overall Score : 86 / 100

Course Description

In the second course of this specialization, we will dive into the components and best practices of a high-performing ML system in production environments. Prerequisites: Basic SQL, familiarity with Python and TensorFlowCOMPLETION CHALLENGEComplete any GCP specialization from November 5 - November 30, 2019 for an opportunity to receive a GCP t-shirt (while supplies last). Check Discussion Forums for details.

Time: 8h

Instructor Details

Google Cloud Training

4.3 Rating
51 Reviews

The Google Cloud Training team is responsible for developing, delivering and evaluating training that enables our enterprise customers and partners to use our products and solution offerings in an effective and impactful way. Google Cloud helps millions of organizations empower their employees, serve their customers, and build what's next for their businesses with innovative technology created in-and for-the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.

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Reviews

4.3

51 total reviews

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By Soroush A on 6-Aug-19

Fantastic experience.

By Maxim on 5-Jul-19

This specialization consists of 5 courses:Course1: End-to-End Machine Learning with TensorFlow on GCPCourse2: Production Machine Learning SystemsCourse3: Image Understanding with TensorFlow on GCPCourse4: Sequence Models for Time Series and Natural Language ProcessingCourse5: Recommendation Systems with TensorFlow on GCPIn specialization's FAQ say nothing about "audit" option. Are You know what is it ? "Audit" means that You can use course video material even after You subscriptions ended. By fact, only "Course 1" has such ability. Before pay for specialization, carefully check FAQ for EACH separated course in specialization:courses 2-5 has special items in FAQ:"Why canÃ¢Â€Â™t I audit this course? This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.""Who have paid" means that after You subscriptions ended, you lost access to video materials in this courses.p.s. 1 star only for "Audit", content and lecturers are rated higher - at least 4 stars

By Alireza K on 29-Sep-19

The Qwiklabs should be more than copy pasting commands. Also I think this course is suitable for people with many years of experience in software development not people like me just came out from university!

By M T on 28-Oct-19

The first module was really good, but the others just seemed like an ad for GCS. Also, the 3rd and 4th module the labs / lab video was hard to follow and felt like I was just reading random code.

By KimNamho on 10-Jul-19

thank you

By Junhwan Y on 30-Jun-19

This course include deep contexts about Machine Learning. But, It's somewhat boring.

By ÃªÂ¹Â€Ã¬ÂœÂ Ã¬ÂƒÂ on 30-Jun-19

Some errors in Kubeflow quicklabs.

By JJ on 16-May-19

While there is definitely some good and useful content in this course, not all of the material is useful. ~40% of the course felt like a sales pitch, at least to me.

By Harold L M M on 8-Nov-18

Overall rating is 3 out of 5, as I expected more of the initial line in the first course. The optional Kubeflow lab has issues, as the ksonnet apply command line halts. Also, the last lab was expected to allow the student to code more, as this is the only way to make a person to gain more insights on the architecture.

By Lloyd P on 6-Jan-19

The module on hybrid systems was weak. The time it would take to cover the material would be prohibitive so why do the intro that then apologize for not having the time to explain the material. Leave it out...

By Mirko J R on 2-Apr-19